Hao Zhang (张皓)

Biography

Currently, Hao is pursuing his M.Sc.'s degree at LAMDA Group led by professor Zhi-Hua Zhou. His adviser is professor Jianxin Wu. Before that, he received his B.Sc.'s degree in Nanjing University, China, in 2016. In the same year, he was admitted to study for a M.Sc.'s degree in Nanjing University without entrance examination. He used to be a member of Excellent Engineer Training Program, from Ministry of Education of China. He is a columnist of AI Era (新智元).

Besides, he has been a member of Communist Party of China since 2013.

Research Interests

Hao's current research interests mainly include machine learning and computer vision, especially on deep learning and visual recognition. He is working on exploiting convolutional features in both supervised and unsupervised ways to improve the efficiency of convolutional neural networks.

Besides, he is particularly interested in linear algebra and its applications. Linear algebra is closely related to real-world applications, such as linear systems (Ax=b), dynamic systems (u_{k+1} = Au_k or du(t)/dt = Au(t)), optimization problems (arg min_u f(u)), linear transformations (T(u)=Au), etc. Please refer to this page for further discussions.

Awards and Honors

• First Place in Apparent Personality Analysis Contest. ECCV, 2016.
• National Scholarship. Ministry of Education of China, 2013.
• Member of Excellent Engineer Training Program, Ministry of Education of China, 2013--2016.
• Excellent All-round Student. Jiangsu Provincial Department of Education, 2015.
• Top-grade in Jiangsu Provincial Undergraduate Electronics Design Contest. Jiangsu Committee of the National Undergraduate Electronics Design Contest, 2014.
• Top-grade "Red Sun" Scholarship. Nanjing Red Sun CO., LTD, 2014. (only 20 from 12,000 undergraduate students in Nanjing University achieved per year)
• Top-grade Graduate Research Scholarship. Nanjing University, 2016, 2017, 2018.
• Excellent Students. Nanjing University, 2014.
• Excellent Cadre of Students. Nanjing University, 2013.
• Excellent Undergraduate Student. Nanjing University, 2016.
• People's Scholarship Speciality Specialization. Nanjing University, 2015.
• People's Scholarship Social Work Specialization. Nanjing University, 2014.
• Second Class Xingquan Responsibility Scholarship. Nanjing University, 2015.
• Excellent League Member (twice). Youth League Committee of Nanjing University, 2014, 2015.
• Excellent Student in Summer Social Practice. Youth League Committee of Nanjing University, 2013.

MOOC and Online Courses

Hao is determined to become a lifelong learner. The followings are the courses he completed.

Reimplementations

Hao has reimplemented several papers, some of which have been open sourced in GitHub.

• Y. Huang, X. Sun, M. Lu, and M. Xu. Channel-max,channel-drop and stochastic max-pooling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 9–17, 2015.
• Y. Kalantidis, C. Mellina, and S. Osindero. Cross-dimensional weighting for aggregated deep convolutional features. In Proceedings of the European Conference of Computer Vision Workshops, pages 685--701, 2016.
• T.-Y. Lin, A. RoyChowdhury, and S. Maji. Bilinear CNN models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pages 1449--1457, 2015.
• X.-S. Wei, C.-L. Zhang, Y. Li, C.-W. Xie, J. Wu, C. Shen, and Z.-H. Zhou. Deep descriptor transforming for image co-localization. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 3048--3054, 2017.
• H. Wu and X. Gu. Max-pooling dropout for regularization of convolutional neural networks. In Proceedings of the International Conference on Neural Information Processing, pages 46--54, 2015.
• J. Xu, C. Shi, C. Qi, C. Wang, and B. Xiao. Part-based weighting aggregation of deep convolutional features for image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1--1, 2018.

Book List

The followings are the books Hao has read.

• Sheldon Axler. Linear algebra done right. Springer, 1997.
• Stephen Boyd and Lieven Vandenberghe. Introduction to applied linear algebra: Vectors, matrices, and least squares. Cambridge University Press, 2018.
• Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford Stein. Introduction to algorithms (3rd edition). MIT Press, 2009. [Solutions]
• Allen Downey, Jeffrey Elkner, and Chris Meyers. How to think like a computer scientist: Learning with Python. Green Tea Press, 2002.
• Allen Downey. Think Python: how to think like a computer scientist. Green Tea Press, 2012. [Online Book]
• Ian Goodfellow, Aaron Courville, and Yoshua Bengio. Deep Learning: Adaptive computation and machine learning series. MIT Press. 2016.
• Rafael Gonzalez, Richard Woods, and Steven Eddin. Digital image processing. Pearson, 2007.
• Rafael Gonzalez, Richard Woods, and Steven Eddin. Digital image processing using MATLAB. Gatesmark Publishing, 2009.
• David C. Lay. Linear Algebra and Its Applications (Fifth Edition). Pearson, 2014.
• Eric Lehman, Thomson Leighton, and Albert Meyer. Mathematics for computer science (2010 version). MIT, 2010.
• Eric Lehman, Thomson Leighton, and Albert Meyer. Mathematics for computer science (2017 version). MIT, 2017.
• Stanley Lippman. Essential C++. Addison-Wesley Longman Publishing Co., Inc., 1999.
• Stanley Lippman, Josée Lajoie, and Barbara Moo. C++ primer (5th edition). Addison-Wesley Professional, 2012. [Errata] [Solution Code]
• Bradley Miller and David Ranum. Problem solving with algorithms and data structures using Python. Franklin, Beedle & Associates Inc., 2006. [Online Book]
• Williams Shotts. The linux command line: A complete introduction. No Starch Press, 2012.
• Gilbert Strang. Introduction to linear algebra (Fourth Edition). Wellesley Cambridge Press, 2009.
• Gilbert Strang. Linear algebra and its applications (Fourth Edition). Academic Press, 2006.
• 刘金鹏. Linux入门很简单. 清华大学出版社, 2012.
• 吴军. 数学之美. 第二版. 人民邮电出版社, 2014.
• 王世江 and 鸟哥. 鸟哥的Linux私房菜:基础学习篇. 第3版. 人民邮电出版社, 2010.
• 周志华. 机器学习. 清华大学出版社, 2016. [勘误修订]

Notes

Convolutional Neuron Networks and its Applications [pdf]

• Convolution Neural Network (CNN) is the state-of-the-art approach to object recognition, and it has show greatly advance on the performance of many compute vision tasks. To have a deep understanding of CNN and to inspire ideas for cutting-edge research, I think the most fundamental and effective way is to look at recent CNN publications from top-tier vision conferences and journals. Therefore, I decided to write a note to take down the basic ideas and my understandings of those publications. At present, this note contains around 60 papers from ICCV, ECCV, CVPR, NIPS, ICML, ICLR and so on. The content covers the basic topics in computer vision including image classification, object localization, object detection, object segmentation, image and language, video classification, GAN, etc.

Notes on Machine Learning [pdf]

• This note was written when I was starting studying machine learning. The first part includes mathematical background such as linear algebra, probability, statistics, information theory, and numerical computation. The second part illustrate the learning theory, foundations of machine learning, and some commonly used learning algorithms. The third part shows some basic idea of deep learning. The whole note is 431 pages long.

Mathematics for Computer Science [pdf]

• This note explains how to use mathematical models and methods to analyze problems that arise in computer science.

C++: Concepts and Practices [pdf]

• This note was written when I was studying C++.

Python Reference Note [pdf]

• This note was written when I was studying Python.

Useful Resources

Machine Learning

Courses

• Trevor Hastie. Statistical Learning. Stanford.
• Yaser Abu-Mostafa. Learning From Data. edX.
• Reza Zadeh. CME323: Distributed Algorithms and Optimization. Stanford.
• Percy Liang. CS229T/STATS231: Statistical Learning Theory. Stanford.
• Jeff Ullman. CS246: Mining Massive Data Sets. Stanford.
• Patrick Winston. 6.034: Artificial Intelligence. MIT.

Books

• John Kelleher and Brian Namee. Fundamentals of Machine Learning for Predictive Data Analytics.
• Andrew Webb and Keith Copsey. Statistical Pattern Recognition, 3rd Edition.
• Christopher Bishop. Pattern Recognition and Machine Learning. Springer. 2006.
• Kevin Murphy. Machine Learning: A Probabilistic Perspective. MIT Press. 2012.
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition. Springer. 2009.
• Thom Mitchell. Machine Learning. McGraw Hill Higher Education. 1997.

Deep Learning Courses and Books

• Geoffrey Hinton. Neural Networks for Machine Learning. Coursera.
• Nando de Freitas. Machine Learning. Oxford.
• Sergey Levine, John Schulman, and Chelsea Finn. CS 294: Deep Reinforcement Learning. Berkeley.
• Lex Fridman. 6.S094: Deep Learning for Self-Driving Cars. MIT.
• Nick Locascio et al. 6.S191: Introduction to Deep Learning. MIT.
• David Silver. Reinforcement Learning. UCL.
• Michael Nielsen. Neural Networks and Deep Learning. Online Book.

Math

Linear Algebra

• Gilbert Strang. 18.085 Computational Science and Engineering I. MIT.
• Gilbert Strang. 18.086 Computational Science and Engineering II. MIT.
• Philip Klein. Coding the Matrix Linear Algebra through Computer Science Applications. Coursera.
• Roger Horn and Charles Johnson. Matrix Analysis. 2nd Edition. Cambridge University Press. 2012.
• Kaare Petersen and Michael Pedersen. Matrix Cookbook
• Trefethen Lloyd and David Bau. Numerical Linear Algebra.

Probability and Statistics

• Larry Wasserman. STAT705 Intermediate Statistics. CMU.
• Morris DeGroot and Mark Schervish. Probability and Statistics.
• Robert Hogg, Joseoh McKean, and Allen Craig. Introduction to Mathematical Statistics. Seventh Edition. 机械工业出版社. 2012.
• Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press. 2009.

C++

• Andrew Koenig, Barbara Moo. Ruminations on C++: A Decade of Programming Insight and Experience. Addison-Wesley Professional. 1996.
• Scott Meyers. Effective C++: 55 Specific Ways to Improve Your Programs and Designs. 3rd Edition. Addison-Wesley Professional. 2005.
• Scott Meyers. More Effective C++: 35 New Ways to Improve Your Programs and Designs. Addison-Wesley Professional. 1996.
• Eric Freeman, Elisabeth Robson, Bert Bates, and Kathy Sierra. Head First Design Patterns. O'Reilly Media. 2014.
• Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional. 1994.

Python

• Martelli, A., Ravenscroft, A., & Ascher, D. (2005). Python cookbook. O'Reilly Media.
• Brett Slatkin. Effective Python: 59 Specific Ways to Write Better Python (Effective Software Development Series). Addison-Wesley Professional. 2015.
• Micha Gorelick & Ian Ozsvald. High Performance Python: Practical Performant Programming for Humans. O'Reilly Media. 2014.

Linux Operating System

• 赵鑫磊 and 张洁. Linux就是这个范儿. 人民邮电出版社. 2014.
• Linus Torvalds and David Diamond. Just for Fun: The Story of an Accidental Revolutionary. Collins Business. 2002.
• Drew Neil. Practical Vim:Edit Text at the Speed of Thought. 2nd Revised edition. The Pragmatic Programmers. 2015.
• A Vim Tutorial and Primer.
• 鸟哥. 鸟哥的Linux私房菜:服务器架设篇. 第3版. 机械工业出版社. 2012.
• Richard Stevens and Stephen Rago. Advanced Programming in the UNIX Environment. 3rd Edition. Addison-Wesley Professional. 2013.
• Richard Stevens, Bill Fenner, and Andrew Rudoff. Unix Network Programming, Volume 1: The Sockets Networking API. 3rd Edition. Addison-Wesley Professional. 2003.
• Richard Stevens. UNIX Network Programming, Volume 2: Interprocess Communications. 2nd Edition. Addison-Wesley Professional.

Miscellaneous

• Sam Altman. CS183B: How to Start a Startup. Stanford.

Hobbies

Reading

Reading a good book is like talking to a wise man, which leads to endless aftertastes. The followings are the ones Hao has read.

大众哲学(艾思奇), 推拿(毕飞宇), 雷雨(曹禺), 白鹿原(陈忠实), 明朝那些事儿(当年明月), 高兴(贾平凹), 47楼207: 北大醉侠的浪漫宣言(孔庆东), 茶馆(老舍), 北平无战事(刘和平), 我不是潘金莲(刘震云), 我叫刘跃进(刘震云), 阿Q正传(鲁迅), 朝花夕拾(鲁迅), 平凡的世界(路遥), 三国演义(罗贯中), 围城(钱钟书), 活着(余华), 傲慢与偏见(奥斯汀), 理智与情感(奥斯汀), 动物庄园(奥威尔), 第二性(波伏娃), 简·爱(勃朗特), 一封陌生女子的来信(茨威格), 我的职业是小说家(村上春树), 包法利夫人(福楼拜), 南方与北方(盖斯凯尔), 霍乱时期的爱情(马尔克斯), 飘(米歇尔), 罗密欧与朱丽叶(莎士比亚), 堂吉诃德(塞万提斯), 小王子(圣埃克苏佩里), 红与黑(司汤达), 安娜·卡列尼娜(托尔斯泰), 悲惨世界(雨果).

Fitness, Boxing, and Nunchaku

Research sometimes can be exhausting, fitness lets one be in a good state.

Photography

A good photograph shows the emotion of the photographer. Photography is a kind of artistic creation --- painting is doing addition, while photography is doing subtraction.

BBC Documentary

Astonishing discoveries of wild life of animals.

生命故事(Life Story), 荒野间谍(Spy in the Wild), 猫的秘密生活(The Secret Life of the Cat).

Competitive Games

Including traditional ones like chess, Chinese chess, and go, and electronic sports like Warcraft and Starcraft.

Correspondence

Mail

National Key Laboratory for Novel Software Technology

Nanjing University, Xianlin Campus

163 Xianlin Avenue, Qixia District

Nanjing, Jiangsu Province

210023, China

Laboratory

328, Computer Science and Technology Building, Xianlin Campus, Nanjing University

Homepage

http://lamda.nju.edu.cn/zhangh/

https://sites.google.com/view/haozhang/

(These two pages are identical)